Syllabus for Intelligent Interactive Systems

Intelligenta interaktiva system

  • 5 credits
  • Course code: 1MD032
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N, Human-Computer Interaction A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2015-03-12
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2019
  • Entry requirements:

    120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • select appropriate computational techniques and machine learning methods and write programs that use these for automatic detection of human behaviours and states
  • determine appropriate design approaches to build social perception abilities such as recognising humans, behaviours and higher level social states and variables (e.g., emotions) based on behavioural features
  • describe basic principles of socially adaptive behaviour in robots and embodied interactive systems
  • apply basic principles of design and evaluation of human-machine interaction
  • evaluate the impact that affect recognition, behaviour detection, and similar technologies may have on ethical values like privacy and autonomy, and to suggest strategies for fulfiling values that are important for users and society at large, including minimisation of negative consequences


Topics include face detection and tracking, facial feature detection and tracking, facial expression and gesture recognition, automatic analysis of multimodal behaviour, automatic inference of affect and social signals, reinforcement learning for adaptive machines, machine embodiment and behaviour generation, control and planning, human-agent and human-robot interaction.


Lectures and tutoring.


Written assignments, oral and written presentation of a project.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

The reading list is missing. For further information, please contact the responsible department.